Overview

Dataset statistics

Number of variables18
Number of observations25976
Missing cells0
Missing cells (%)0.0%
Duplicate rows42
Duplicate rows (%)0.2%
Total size in memory3.1 MiB
Average record size in memory124.0 B

Variable types

Categorical8
Numeric10

Alerts

Dataset has 42 (0.2%) duplicate rowsDuplicates
Baggage handling is highly overall correlated with Inflight service and 1 other fieldsHigh correlation
Class is highly overall correlated with Type of TravelHigh correlation
Cleanliness is highly overall correlated with Food and drink and 2 other fieldsHigh correlation
Food and drink is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Inflight entertainment is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Inflight service is highly overall correlated with Baggage handling and 1 other fieldsHigh correlation
Inflight wifi service is highly overall correlated with satisfactionHigh correlation
On-board service is highly overall correlated with Baggage handling and 1 other fieldsHigh correlation
Online boarding is highly overall correlated with satisfactionHigh correlation
Seat comfort is highly overall correlated with Cleanliness and 2 other fieldsHigh correlation
Type of Travel is highly overall correlated with ClassHigh correlation
satisfaction is highly overall correlated with Inflight wifi service and 1 other fieldsHigh correlation
Inflight wifi service has 813 (3.1%) zerosZeros
Online boarding has 652 (2.5%) zerosZeros
Departure Delay in Minutes has 14688 (56.5%) zerosZeros

Reproduction

Analysis started2024-06-14 06:37:53.573473
Analysis finished2024-06-14 06:38:11.932443
Duration18.36 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
0
13172 
1
12804 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 13172
50.7%
1 12804
49.3%

Length

2024-06-14T12:08:12.077082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:12.229674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13172
50.7%
1 12804
49.3%

Most occurring characters

ValueCountFrequency (%)
0 13172
50.7%
1 12804
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13172
50.7%
1 12804
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13172
50.7%
1 12804
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13172
50.7%
1 12804
49.3%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
0
21177 
1
4799 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21177
81.5%
1 4799
 
18.5%

Length

2024-06-14T12:08:12.381271image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:12.520869image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 21177
81.5%
1 4799
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0 21177
81.5%
1 4799
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21177
81.5%
1 4799
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21177
81.5%
1 4799
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21177
81.5%
1 4799
 
18.5%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.620958
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:12.697423image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.135685
Coefficient of variation (CV)0.3820121
Kurtosis-0.71787731
Mean39.620958
Median Absolute Deviation (MAD)12
Skewness-8.7986003 × 10-5
Sum1029194
Variance229.08897
MonotonicityNot monotonic
2024-06-14T12:08:12.920799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 723
 
2.8%
25 713
 
2.7%
40 635
 
2.4%
41 633
 
2.4%
43 626
 
2.4%
44 622
 
2.4%
45 600
 
2.3%
27 595
 
2.3%
23 589
 
2.3%
48 580
 
2.2%
Other values (65) 19660
75.7%
ValueCountFrequency (%)
7 123
0.5%
8 157
0.6%
9 167
0.6%
10 139
0.5%
11 159
0.6%
12 159
0.6%
13 173
0.7%
14 153
0.6%
15 188
0.7%
16 257
1.0%
ValueCountFrequency (%)
85 8
 
< 0.1%
80 32
0.1%
79 10
 
< 0.1%
78 11
 
< 0.1%
77 19
 
0.1%
76 15
 
0.1%
75 15
 
0.1%
74 14
 
0.1%
73 16
 
0.1%
72 48
0.2%

Type of Travel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
0
18038 
1
7938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18038
69.4%
1 7938
30.6%

Length

2024-06-14T12:08:13.127246image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:13.266902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 18038
69.4%
1 7938
30.6%

Most occurring characters

ValueCountFrequency (%)
0 18038
69.4%
1 7938
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18038
69.4%
1 7938
30.6%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18038
69.4%
1 7938
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18038
69.4%
1 7938
30.6%

Class
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
0
12495 
1
11564 
2
1917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12495
48.1%
1 11564
44.5%
2 1917
 
7.4%

Length

2024-06-14T12:08:13.419493image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:13.652842image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12495
48.1%
1 11564
44.5%
2 1917
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 12495
48.1%
1 11564
44.5%
2 1917
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12495
48.1%
1 11564
44.5%
2 1917
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12495
48.1%
1 11564
44.5%
2 1917
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12495
48.1%
1 11564
44.5%
2 1917
 
7.4%

Inflight wifi service
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7247459
Minimum0
Maximum5
Zeros813
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:13.834356image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3353841
Coefficient of variation (CV)0.49009489
Kurtosis-0.85825256
Mean2.7247459
Median Absolute Deviation (MAD)1
Skewness0.04079167
Sum70778
Variance1.7832506
MonotonicityNot monotonic
2024-06-14T12:08:13.993957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 6490
25.0%
3 6317
24.3%
4 4981
19.2%
1 4488
17.3%
5 2887
11.1%
0 813
 
3.1%
ValueCountFrequency (%)
0 813
 
3.1%
1 4488
17.3%
2 6490
25.0%
3 6317
24.3%
4 4981
19.2%
5 2887
11.1%
ValueCountFrequency (%)
5 2887
11.1%
4 4981
19.2%
3 6317
24.3%
2 6490
25.0%
1 4488
17.3%
0 813
 
3.1%

Food and drink
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2153526
Minimum0
Maximum5
Zeros25
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:14.144554image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3315064
Coefficient of variation (CV)0.41410897
Kurtosis-1.1446545
Mean3.2153526
Median Absolute Deviation (MAD)1
Skewness-0.17022924
Sum83522
Variance1.7729092
MonotonicityNot monotonic
2024-06-14T12:08:14.299140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6204
23.9%
5 5644
21.7%
3 5494
21.2%
2 5395
20.8%
1 3214
12.4%
0 25
 
0.1%
ValueCountFrequency (%)
0 25
 
0.1%
1 3214
12.4%
2 5395
20.8%
3 5494
21.2%
4 6204
23.9%
5 5644
21.7%
ValueCountFrequency (%)
5 5644
21.7%
4 6204
23.9%
3 5494
21.2%
2 5395
20.8%
1 3214
12.4%
0 25
 
0.1%

Online boarding
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2616646
Minimum0
Maximum5
Zeros652
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:14.447745image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3555357
Coefficient of variation (CV)0.41559628
Kurtosis-0.68504294
Mean3.2616646
Median Absolute Deviation (MAD)1
Skewness-0.46921756
Sum84725
Variance1.837477
MonotonicityNot monotonic
2024-06-14T12:08:14.598340image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 7706
29.7%
3 5313
20.5%
5 5307
20.4%
2 4429
17.1%
1 2569
 
9.9%
0 652
 
2.5%
ValueCountFrequency (%)
0 652
 
2.5%
1 2569
 
9.9%
2 4429
17.1%
3 5313
20.5%
4 7706
29.7%
5 5307
20.4%
ValueCountFrequency (%)
5 5307
20.4%
4 7706
29.7%
3 5313
20.5%
2 4429
17.1%
1 2569
 
9.9%
0 652
 
2.5%

Seat comfort
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
4
7991 
5
6688 
3
4632 
2
3632 
1
3033 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
4 7991
30.8%
5 6688
25.7%
3 4632
17.8%
2 3632
14.0%
1 3033
 
11.7%

Length

2024-06-14T12:08:14.769881image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:14.940398image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 7991
30.8%
5 6688
25.7%
3 4632
17.8%
2 3632
14.0%
1 3033
 
11.7%

Most occurring characters

ValueCountFrequency (%)
4 7991
30.8%
5 6688
25.7%
3 4632
17.8%
2 3632
14.0%
1 3033
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7991
30.8%
5 6688
25.7%
3 4632
17.8%
2 3632
14.0%
1 3033
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7991
30.8%
5 6688
25.7%
3 4632
17.8%
2 3632
14.0%
1 3033
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7991
30.8%
5 6688
25.7%
3 4632
17.8%
2 3632
14.0%
1 3033
 
11.7%

Inflight entertainment
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3577533
Minimum0
Maximum5
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:15.099004image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3382994
Coefficient of variation (CV)0.39856989
Kurtosis-1.0628345
Mean3.3577533
Median Absolute Deviation (MAD)1
Skewness-0.37135337
Sum87221
Variance1.7910452
MonotonicityNot monotonic
2024-06-14T12:08:15.264532image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 7368
28.4%
5 6331
24.4%
3 4745
18.3%
2 4331
16.7%
1 3197
12.3%
0 4
 
< 0.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3197
12.3%
2 4331
16.7%
3 4745
18.3%
4 7368
28.4%
5 6331
24.4%
ValueCountFrequency (%)
5 6331
24.4%
4 7368
28.4%
3 4745
18.3%
2 4331
16.7%
1 3197
12.3%
0 4
 
< 0.1%

On-board service
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3856637
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:15.418150image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2820884
Coefficient of variation (CV)0.37868155
Kurtosis-0.87470085
Mean3.3856637
Median Absolute Deviation (MAD)1
Skewness-0.42650654
Sum87946
Variance1.6437506
MonotonicityNot monotonic
2024-06-14T12:08:15.566751image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 7836
30.2%
5 5844
22.5%
3 5709
22.0%
2 3670
14.1%
1 2915
 
11.2%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 2915
 
11.2%
2 3670
14.1%
3 5709
22.0%
4 7836
30.2%
5 5844
22.5%
ValueCountFrequency (%)
5 5844
22.5%
4 7836
30.2%
3 5709
22.0%
2 3670
14.1%
1 2915
 
11.2%
0 2
 
< 0.1%

Leg room service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3501694
Minimum0
Maximum5
Zeros126
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:15.710366image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3188623
Coefficient of variation (CV)0.39367034
Kurtosis-0.99392241
Mean3.3501694
Median Absolute Deviation (MAD)1
Skewness-0.34120952
Sum87024
Variance1.7393979
MonotonicityNot monotonic
2024-06-14T12:08:15.865951image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 7097
27.3%
5 6238
24.0%
2 5015
19.3%
3 4958
19.1%
1 2542
 
9.8%
0 126
 
0.5%
ValueCountFrequency (%)
0 126
 
0.5%
1 2542
 
9.8%
2 5015
19.3%
3 4958
19.1%
4 7097
27.3%
5 6238
24.0%
ValueCountFrequency (%)
5 6238
24.0%
4 7097
27.3%
3 4958
19.1%
2 5015
19.3%
1 2542
 
9.8%
0 126
 
0.5%

Baggage handling
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
4
9378 
5
6747 
3
5219 
2
2841 
1
1791 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 9378
36.1%
5 6747
26.0%
3 5219
20.1%
2 2841
 
10.9%
1 1791
 
6.9%

Length

2024-06-14T12:08:16.042451image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:16.202024image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 9378
36.1%
5 6747
26.0%
3 5219
20.1%
2 2841
 
10.9%
1 1791
 
6.9%

Most occurring characters

ValueCountFrequency (%)
4 9378
36.1%
5 6747
26.0%
3 5219
20.1%
2 2841
 
10.9%
1 1791
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 9378
36.1%
5 6747
26.0%
3 5219
20.1%
2 2841
 
10.9%
1 1791
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 9378
36.1%
5 6747
26.0%
3 5219
20.1%
2 2841
 
10.9%
1 1791
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 9378
36.1%
5 6747
26.0%
3 5219
20.1%
2 2841
 
10.9%
1 1791
 
6.9%

Checkin service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
4
7278 
3
7007 
5
5264 
1
3218 
2
3209 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 7278
28.0%
3 7007
27.0%
5 5264
20.3%
1 3218
12.4%
2 3209
12.4%

Length

2024-06-14T12:08:16.376557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:16.529177image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 7278
28.0%
3 7007
27.0%
5 5264
20.3%
1 3218
12.4%
2 3209
12.4%

Most occurring characters

ValueCountFrequency (%)
4 7278
28.0%
3 7007
27.0%
5 5264
20.3%
1 3218
12.4%
2 3209
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7278
28.0%
3 7007
27.0%
5 5264
20.3%
1 3218
12.4%
2 3209
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7278
28.0%
3 7007
27.0%
5 5264
20.3%
1 3218
12.4%
2 3209
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7278
28.0%
3 7007
27.0%
5 5264
20.3%
1 3218
12.4%
2 3209
12.4%

Inflight service
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6492532
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:16.687752image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.180681
Coefficient of variation (CV)0.32354044
Kurtosis-0.36083979
Mean3.6492532
Median Absolute Deviation (MAD)1
Skewness-0.69680264
Sum94793
Variance1.3940075
MonotonicityNot monotonic
2024-06-14T12:08:16.843337image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 9378
36.1%
5 6950
26.8%
3 5017
19.3%
2 2851
 
11.0%
1 1778
 
6.8%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1778
 
6.8%
2 2851
 
11.0%
3 5017
19.3%
4 9378
36.1%
5 6950
26.8%
ValueCountFrequency (%)
5 6950
26.8%
4 9378
36.1%
3 5017
19.3%
2 2851
 
11.0%
1 1778
 
6.8%
0 2
 
< 0.1%

Cleanliness
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2862257
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:17.160462image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3193301
Coefficient of variation (CV)0.40147274
Kurtosis-1.0237346
Mean3.2862257
Median Absolute Deviation (MAD)1
Skewness-0.30428466
Sum85363
Variance1.7406318
MonotonicityNot monotonic
2024-06-14T12:08:17.327017image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6790
26.1%
3 6065
23.3%
5 5727
22.0%
2 3981
15.3%
1 3411
13.1%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 3411
13.1%
2 3981
15.3%
3 6065
23.3%
4 6790
26.1%
5 5727
22.0%
ValueCountFrequency (%)
5 5727
22.0%
4 6790
26.1%
3 6065
23.3%
2 3981
15.3%
1 3411
13.1%
0 2
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ)

ZEROS 

Distinct313
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.30609
Minimum0
Maximum1128
Zeros14688
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size203.1 KiB
2024-06-14T12:08:17.564382image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile74
Maximum1128
Range1128
Interquartile range (IQR)12

Descriptive statistics

Standard deviation37.42316
Coefficient of variation (CV)2.6158901
Kurtosis102.18308
Mean14.30609
Median Absolute Deviation (MAD)0
Skewness7.1939698
Sum371615
Variance1400.4929
MonotonicityNot monotonic
2024-06-14T12:08:17.778809image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14688
56.5%
1 734
 
2.8%
2 581
 
2.2%
3 526
 
2.0%
4 455
 
1.8%
5 444
 
1.7%
6 367
 
1.4%
7 356
 
1.4%
8 323
 
1.2%
10 306
 
1.2%
Other values (303) 7196
27.7%
ValueCountFrequency (%)
0 14688
56.5%
1 734
 
2.8%
2 581
 
2.2%
3 526
 
2.0%
4 455
 
1.8%
5 444
 
1.7%
6 367
 
1.4%
7 356
 
1.4%
8 323
 
1.2%
9 297
 
1.1%
ValueCountFrequency (%)
1128 1
< 0.1%
951 1
< 0.1%
815 1
< 0.1%
794 1
< 0.1%
756 1
< 0.1%
624 1
< 0.1%
590 1
< 0.1%
581 1
< 0.1%
569 1
< 0.1%
565 1
< 0.1%

satisfaction
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size203.1 KiB
0
14573 
1
11403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25976
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 14573
56.1%
1 11403
43.9%

Length

2024-06-14T12:08:17.975311image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T12:08:18.113911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14573
56.1%
1 11403
43.9%

Most occurring characters

ValueCountFrequency (%)
0 14573
56.1%
1 11403
43.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14573
56.1%
1 11403
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common 25976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14573
56.1%
1 11403
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14573
56.1%
1 11403
43.9%

Interactions

2024-06-14T12:08:09.645496image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:55.420532image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:57.024243image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.664885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:00.190778image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:01.844355image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.385235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:04.897190image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:06.435079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.970999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:09.805069image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:55.608030image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:57.227702image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.830441image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:00.491971image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.003955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.542812image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.061750image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:06.594655image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:08.129547image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:09.955666image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:55.765609image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:57.418218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.979015image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:00.637609image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.154530image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.692416image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.213347image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:06.741261image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:08.280147image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:10.109256image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:55.917206image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:57.592751image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:59.124629image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:00.783220image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.306121image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.839049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.365939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:06.892854image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:08.430743image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:10.261876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:56.080766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:57.743322image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:59.279214image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:00.934787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.459710image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.986630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.522519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.038465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:08.581340image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:10.412443image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:56.239343image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:57.894914image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:59.432804image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:01.085384image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.617287image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:04.132236image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.672117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.190089image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:08.731937image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:10.565035image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:56.400910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.045515image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:59.589384image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:01.237976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.769907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:04.280841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.824710image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.371574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:08.887520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:10.714636image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:56.552532image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.201097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:59.738984image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:01.389572image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:02.923497image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:04.432433image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:05.972316image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.523169image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:09.199687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:10.870221image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:56.705097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.353689image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:59.883626image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:01.541168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.076063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:04.587022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:06.129924image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.668808image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:09.348289image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:11.021814image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:56.861680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:07:58.505311image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:00.039181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:01.688798image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:03.226659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:04.734626image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:06.273509image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:07.815388image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-06-14T12:08:09.496918image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-06-14T12:08:18.250547image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
AgeBaggage handlingCheckin serviceClassCleanlinessCustomer TypeDeparture Delay in MinutesFood and drinkGenderInflight entertainmentInflight serviceInflight wifi serviceLeg room serviceOn-board serviceOnline boardingSeat comfortType of Travelsatisfaction
Age1.0000.0570.0410.2140.0480.387-0.0020.0220.0070.074-0.0420.0070.0440.0680.2070.1060.3510.276
Baggage handling0.0571.0000.1430.1420.1180.058-0.0230.0500.0410.4030.6320.1100.3820.5390.1200.0850.0450.292
Checkin service0.0410.1431.0000.1290.1610.026-0.0190.0730.0000.1160.2540.0460.1450.2400.2150.1200.0080.257
Class0.2140.1420.1291.000-0.1460.1210.004-0.0960.013-0.205-0.175-0.035-0.212-0.230-0.3270.1750.5550.498
Cleanliness0.0480.1180.161-0.1461.0000.091-0.0240.6470.0210.6830.1100.1220.0970.1210.3360.5700.1150.323
Customer Type0.3870.0580.0260.1210.0911.0000.005-0.0440.027-0.0880.0180.004-0.045-0.049-0.1900.1550.3080.179
Departure Delay in Minutes-0.002-0.023-0.0190.004-0.0240.0051.000-0.0220.013-0.033-0.044-0.023-0.005-0.031-0.0320.0160.0000.025
Food and drink0.0220.0500.073-0.0960.647-0.044-0.0221.0000.0130.6130.0510.1200.0330.0510.2360.5190.1050.230
Gender0.0070.0410.0000.0130.0210.0270.0130.0131.000-0.0050.028-0.0060.026-0.001-0.0530.0570.0190.004
Inflight entertainment0.0740.4030.116-0.2050.683-0.088-0.0330.613-0.0051.0000.4290.1930.3160.4310.2970.5380.1840.419
Inflight service-0.0420.6320.254-0.1750.1100.018-0.0440.0510.0280.4291.0000.1050.3760.5710.1050.0790.0430.282
Inflight wifi service0.0070.1100.046-0.0350.1220.004-0.0230.120-0.0060.1930.1051.0000.1500.1090.4360.1240.1840.526
Leg room service0.0440.3820.145-0.2120.097-0.045-0.0050.0330.0260.3160.3760.1501.0000.3730.1330.0750.1730.336
On-board service0.0680.5390.240-0.2300.121-0.049-0.0310.051-0.0010.4310.5710.1090.3731.0000.1710.0840.0890.331
Online boarding0.2070.1200.215-0.3270.336-0.190-0.0320.236-0.0530.2970.1050.4360.1330.1711.0000.2860.2290.614
Seat comfort0.1060.0850.1200.1750.5700.1550.0160.5190.0570.5380.0790.1240.0750.0840.2861.0000.1480.382
Type of Travel0.3510.0450.0080.5550.1150.3080.0000.1050.0190.1840.0430.1840.1730.0890.2290.1481.0000.453
satisfaction0.2760.2920.2570.4980.3230.1790.0250.2300.0040.4190.2820.5260.3360.3310.6140.3820.4531.000

Missing values

2024-06-14T12:08:11.258181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-14T12:08:11.701024image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderCustomer TypeAgeType of TravelClassInflight wifi serviceFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in Minutessatisfaction
000520153435555255501
10036001545444434501
21120012222241322200
31044000344111131401
40049012412222242401
51016013553543112501
60077005355555545301
700430024454444543771
81047015555522533511
900460023444444544281
GenderCustomer TypeAgeType of TravelClassInflight wifi serviceFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in Minutessatisfaction
259661142014343331223300
259670139001212253444200
2596810410122222233232150
259691052003344444434301
259700136011414452523400
259711134003434432445400
259721023004444445555401
259730017112212243454200
259741014003444432545401
259750042112422112111100

Duplicate rows

Most frequently occurring

GenderCustomer TypeAgeType of TravelClassInflight wifi serviceFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in Minutessatisfaction# duplicates
3210500024444444344013
000280025555335355012
100280043333445453012
200300024444424554012
300430013555555555012
400440024455555453012
500460013445555353012
600460023545555353012
700460035544444545012
800470033455555455012